QA Automation Engineer
2 days ago
We are seeking a QA Automation Engineer with hands-on experience in AI-powered testing and automation frameworks. You will design, implement, and maintain robust automated test suites while leveraging AI/ML tools to improve coverage, defect prediction, and test efficiency across the SDLC.
Key Responsibilities
- Develop and maintain automated test scripts for web, mobile, and API applications using tools like Selenium, Cypress, Playwright, or TestNG.
- Integrate AI/ML frameworks to optimize testing processes (e.g., intelligent test case generation, flakiness detection, root cause analysis).
- Implement continuous testing pipelines within CI/CD environments (e.g., Jenkins, GitHub Actions, GitLab CI).
- Collaborate with developers, data scientists, and product managers to define test strategies for AI/ML-driven features and models.
- Validate data pipelines, model outputs, and predictions for accuracy and fairness.
- Utilize AI-based testing tools such as Testim.io, Applitools, Functionize, or Mabl to enhance automation reliability.
- Monitor and report on test metrics, coverage, and defect trends using AI analytics dashboards.
- Participate in exploratory and regression testing using a risk-based approach.
- Bachelor's degree in Computer Science, Engineering, or a related field.
- 3-7 years of QA experience with a focus on automation testing.
- Experience with AI-assisted testing tools or integrating ML algorithms for test optimization.
- Strong knowledge of Python, Java, or JavaScript for automation scripting.
- Familiarity with REST API testing (e.g., Postman, RestAssured).
- Proficiency in CI/CD pipelines and version control (Git).
- Understanding of data validation and model testing in AI workflows.
- Experience with AI/ML model validation, data drift detection, or synthetic data generation.
- Knowledge of cloud environments (AWS, Azure, GCP) and container orchestration (Docker, Kubernetes).
- Exposure to AI-driven DevOps (AIOps) or MLOps practices.
- Certification in ISTQB, AWS AI Practitioner, or similar is a plus.
- AI Testing Tools: Testim.io, Applitools Eyes, Functionize, Mabl
- ML Frameworks: TensorFlow, PyTorch, Scikit-learn (for model validation)
- Automation Tools: Selenium, Playwright, Cypress, PyTest
- Monitoring & Analytics: Datadog, Grafana, ELK with AI anomaly detection
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